Classification

This guide walks through the process of determining the best approach for building a classifier with Claude and the essentials of end-to-end deployment for a Claude classifier, from use case exploration to back-end integration.

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When to use Claude for classification

When should you consider using an LLM instead of a traditional ML approach for your classification tasks? Here are some key indicators:

1. Rule-based classes: Use Claude when classes are defined by conditions rather than examples, as it can understand underlying rules.
2. Evolving classes: Claude adapts well to new or changing domains with emerging classes and shifting boundaries.
3. Unstructured inputs: Claude can handle large volumes of unstructured text inputs of varying lengths.
4. Limited labeled examples: With few-shot learning capabilities, Claude learns accurately from limited labeled training data.
5. Reasoning Requirements: Claude excels at classification tasks requiring semantic understanding, context, and higher-level reasoning.

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Establish your classification use case

Below is a non-exhaustive list of common classification use cases where Claude excels by industry.

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Implement Claude for classification

The three key model decision factors are: intelligence, latency, and price.

For classification, a smaller model like Claude 3 Haiku is typically ideal due to its speed and efficiency. Though, for classification tasks where specialized knowledge or complex reasoning is required, Sonnet or Opus may be a better choice. Learn more about how Opus, Sonnet, and Haiku compare here.

Use evaluations to gauge whether a Claude model is performing well enough to launch into production.

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1. Build a strong input prompt

While Claude offers high-level baseline performance out of the box, a strong input prompt helps get the best results.

For a generic classifier that you can adapt to your specific use case, copy the starter prompt below:

You will be building a text classifier that can automatically categorize text into a set of predefined categories. 
Here are the categories the classifier will use:

<categories>
{{CATEGORIES}}
</categories>

To help you understand how to classify text into these categories, here are some example texts that have already been labeled with their correct category:

<examples>
{{EXAMPLES}}
</examples>

Please carefully study these examples to identify the key features and characteristics that define each category. Write out your analysis of each category inside <category_analysis> tags, explaining the main topics, themes, writing styles, etc. that seem to be associated with each one.

Once you feel you have a good grasp of the categories, your task is to build a classifier that can take in new, unlabeled texts and output a prediction of which category it most likely belongs to.

Before giving your final classification, show your step-by-step process and reasoning inside <classification_process> tags. Weigh the evidence for each potential category.

Then output your final <classification> for which category you think the example text belongs to.

The goal is to build a classifier that can accurately categorize new texts into the most appropriate category, as defined by the examples.

We also provide a wide range of prompts to get you started in our prompt library, including prompts for a number of classification use cases, including:

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2. Develop your test cases

To run your classification evaluation, you will need test cases to run it on. Take a look at our guide to developing test cases.

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3. Run your eval

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Evaluation metrics

Some success metrics to consider evaluating Claude’s performance on a classification task include:

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Deploy your classifier

To see code examples of how to use Claude for classification, check out the Classification Guide in the Anthropic Cookbook.